{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>tag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>4141</td>\n",
       "      <td>Mark Waters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65</td>\n",
       "      <td>208</td>\n",
       "      <td>dark hero</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65</td>\n",
       "      <td>353</td>\n",
       "      <td>dark hero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65</td>\n",
       "      <td>521</td>\n",
       "      <td>noir thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65</td>\n",
       "      <td>592</td>\n",
       "      <td>dark hero</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId            tag\n",
       "0      18     4141    Mark Waters\n",
       "1      65      208      dark hero\n",
       "2      65      353      dark hero\n",
       "3      65      521  noir thriller\n",
       "4      65      592      dark hero"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tag = pd.read_csv('./data/tag.csv',usecols=[0,1,2])\n",
    "df_tag.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>tag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65</td>\n",
       "      <td>208</td>\n",
       "      <td>dark hero</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65</td>\n",
       "      <td>353</td>\n",
       "      <td>dark hero</td>\n",
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       "      <th>3</th>\n",
       "      <td>65</td>\n",
       "      <td>521</td>\n",
       "      <td>noir thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65</td>\n",
       "      <td>592</td>\n",
       "      <td>dark hero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>65</td>\n",
       "      <td>668</td>\n",
       "      <td>bollywood</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId            tag\n",
       "1      65      208      dark hero\n",
       "2      65      353      dark hero\n",
       "3      65      521  noir thriller\n",
       "4      65      592      dark hero\n",
       "5      65      668      bollywood"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_value_counts = df_tag['tag'].value_counts()\n",
    "top_tag = tag_value_counts[\n",
    "    tag_value_counts >=20\n",
    "].index.tolist()\n",
    "df_tag = df_tag[df_tag['tag'].isin(top_tag)]\n",
    "df_tag.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sci-fi',\n",
       " 'based on a book',\n",
       " 'atmospheric',\n",
       " 'comedy',\n",
       " 'action',\n",
       " 'surreal',\n",
       " 'BD-R',\n",
       " 'twist ending',\n",
       " 'funny',\n",
       " 'dystopia',\n",
       " 'stylized',\n",
       " 'quirky',\n",
       " 'dark comedy',\n",
       " 'classic',\n",
       " 'psychology',\n",
       " 'fantasy',\n",
       " 'time travel',\n",
       " 'romance',\n",
       " 'visually appealing',\n",
       " 'disturbing',\n",
       " 'aliens',\n",
       " 'thought-provoking',\n",
       " 'social commentary',\n",
       " 'Nudity (Topless)',\n",
       " 'violence',\n",
       " 'drugs',\n",
       " 'Criterion',\n",
       " 'true story',\n",
       " 'nudity (topless)',\n",
       " 'adventure',\n",
       " 'animation',\n",
       " 'imdb top 250',\n",
       " 'space',\n",
       " 'CLV',\n",
       " 'dark',\n",
       " 'superhero',\n",
       " 'black comedy',\n",
       " 'post-apocalyptic',\n",
       " 'World War II',\n",
       " 'Betamax',\n",
       " 'cult film',\n",
       " 'satire',\n",
       " 'tense',\n",
       " 'thriller',\n",
       " 'drama',\n",
       " 'predictable',\n",
       " 'politics',\n",
       " 'adapted from:book',\n",
       " 'bittersweet',\n",
       " 'horror',\n",
       " 'based on a true story',\n",
       " 'revenge',\n",
       " 'boring',\n",
       " 'cinematography',\n",
       " 'serial killer',\n",
       " 'great soundtrack',\n",
       " 'music',\n",
       " 'coming of age',\n",
       " 'high school',\n",
       " 'comic book',\n",
       " 'religion',\n",
       " 'violent',\n",
       " 'zombies',\n",
       " 'DVD-Video',\n",
       " 'mental illness',\n",
       " 'anime',\n",
       " 'franchise',\n",
       " 'dreamlike',\n",
       " 'alternate reality',\n",
       " 'friendship',\n",
       " 'murder',\n",
       " 'crime',\n",
       " 'war',\n",
       " 'Quentin Tarantino',\n",
       " 'nonlinear',\n",
       " 'Oscar (Best Picture)',\n",
       " 'psychological',\n",
       " 'parody',\n",
       " 'suspense',\n",
       " 'less than 300 ratings',\n",
       " 'black and white',\n",
       " 'R',\n",
       " \"erlend's DVDs\",\n",
       " 'Johnny Depp',\n",
       " 'remake',\n",
       " 'magic',\n",
       " 'martial arts',\n",
       " 'Disney',\n",
       " 'multiple storylines',\n",
       " 'Brad Pitt',\n",
       " 'Bruce Willis',\n",
       " 'witty',\n",
       " \"Tumey's DVDs\",\n",
       " 'humorous',\n",
       " 'seen more than once',\n",
       " 'inspirational',\n",
       " 'stupid',\n",
       " 'mystery',\n",
       " 'family',\n",
       " 'robots',\n",
       " 'police',\n",
       " 'hilarious',\n",
       " 'slow',\n",
       " 'history',\n",
       " 'Tom Hanks',\n",
       " 'DVD-RAM',\n",
       " 'organized crime',\n",
       " 'musical',\n",
       " 'great acting',\n",
       " 'Morgan Freeman',\n",
       " 'sequel',\n",
       " 'Japan',\n",
       " 'ensemble cast',\n",
       " 'Pixar',\n",
       " 'vampires',\n",
       " 'New York City',\n",
       " 'overrated',\n",
       " 'mindfuck',\n",
       " 'documentary',\n",
       " 'espionage',\n",
       " 'Nudity (Full Frontal)',\n",
       " 'Nudity (Topless - Brief)',\n",
       " 'future',\n",
       " 'artificial intelligence',\n",
       " 'Nudity (Topless - Notable)',\n",
       " 'philosophy',\n",
       " '70mm',\n",
       " 'corruption',\n",
       " 'historical',\n",
       " 'Steven Spielberg',\n",
       " 'gay',\n",
       " 'beautiful',\n",
       " 'Edward Norton',\n",
       " 'satirical',\n",
       " 'movie to see',\n",
       " 'emotional',\n",
       " 'sexuality',\n",
       " 'Bill Murray',\n",
       " 'assassin',\n",
       " 'Christian Bale',\n",
       " 'dialogue',\n",
       " 'soundtrack',\n",
       " 'father-son relationship',\n",
       " 'original',\n",
       " 'fairy tale',\n",
       " 'Leonardo DiCaprio',\n",
       " 'sports',\n",
       " 'relationships',\n",
       " 'England',\n",
       " 'prison',\n",
       " 'lesbian',\n",
       " 'racism',\n",
       " 'Clint Eastwood',\n",
       " 'road trip',\n",
       " 'military',\n",
       " 'Tim Burton',\n",
       " 'acting',\n",
       " 'British',\n",
       " 'loneliness',\n",
       " 'rape',\n",
       " 'chick flick',\n",
       " 'story',\n",
       " 'Samuel L. Jackson',\n",
       " 'weird',\n",
       " 'Nudity (Full Frontal - Notable)',\n",
       " 'bad acting',\n",
       " 'Jim Carrey',\n",
       " 'Tom Cruise',\n",
       " 'silly',\n",
       " 'BD-Video',\n",
       " 'Matt Damon',\n",
       " 'depressing',\n",
       " 'Action',\n",
       " 'love',\n",
       " 'gore',\n",
       " 'Robert Downey Jr.',\n",
       " 'Stanley Kubrick',\n",
       " 'heist',\n",
       " 'whimsical',\n",
       " 'Natalie Portman',\n",
       " 'Robert De Niro',\n",
       " 'Harrison Ford',\n",
       " 'musicians',\n",
       " 'ghosts',\n",
       " 'small town',\n",
       " 'conspiracy',\n",
       " 'Christmas',\n",
       " 'biography',\n",
       " 'great ending',\n",
       " 'Robin Williams',\n",
       " 'creepy',\n",
       " 'Studio Ghibli',\n",
       " 'talking animals',\n",
       " 'philosophical',\n",
       " 'touching',\n",
       " 'might like',\n",
       " 'teen',\n",
       " 'cyberpunk',\n",
       " 'Martin Scorsese',\n",
       " 'gothic',\n",
       " 'Arnold Schwarzenegger',\n",
       " 'Bechdel Test:Fail',\n",
       " 'nudity (full frontal)',\n",
       " 'Coen Brothers',\n",
       " 'Jack Nicholson',\n",
       " 'reviewed',\n",
       " 'suicide',\n",
       " 'Nicolas Cage',\n",
       " 'Al Pacino',\n",
       " 'To See',\n",
       " 'campy',\n",
       " 'survival',\n",
       " 'Scarlett Johansson',\n",
       " 'library',\n",
       " 'clever',\n",
       " 'narrated',\n",
       " 'reflective',\n",
       " 'Kevin Spacey',\n",
       " 'National Film Registry',\n",
       " 'science fiction',\n",
       " 'Christianity',\n",
       " 'long',\n",
       " 'Romance',\n",
       " 'complicated',\n",
       " 'controversial',\n",
       " 'easily confused with other movie(s) (title)',\n",
       " 'George Clooney',\n",
       " 'based on a play',\n",
       " 'Nazis',\n",
       " 'adultery',\n",
       " 'directorial debut',\n",
       " 'heartwarming',\n",
       " 'noir thriller',\n",
       " 'surrealism',\n",
       " 'apocalypse',\n",
       " 'gritty',\n",
       " 'futuristic',\n",
       " 'rock and roll',\n",
       " 'Keanu Reeves',\n",
       " 'memory',\n",
       " 'virtual reality',\n",
       " 'Alfred Hitchcock',\n",
       " 'Woody Allen',\n",
       " 'Stephen King',\n",
       " 'queer',\n",
       " 'Oscar (Best Cinematography)',\n",
       " 'DVD-R',\n",
       " 'supernatural',\n",
       " 'netflix',\n",
       " 'dark humor',\n",
       " 'children',\n",
       " 'love story',\n",
       " 'hallucinatory',\n",
       " 'visually stunning',\n",
       " 'perrot library',\n",
       " 'Paris',\n",
       " 'low budget',\n",
       " 'ClearPlay',\n",
       " 'death',\n",
       " 'France',\n",
       " 'Comedy',\n",
       " 'In Netflix queue',\n",
       " 'Bechdel Test:Pass',\n",
       " \"Can't remember\",\n",
       " 'Netflix Finland',\n",
       " 'dvd',\n",
       " 'Bibliothek',\n",
       " 'terrorism',\n",
       " 'too long',\n",
       " 'Will Smith',\n",
       " 'journalism',\n",
       " '1980s',\n",
       " 'western',\n",
       " 'cliche',\n",
       " 'short',\n",
       " \"memasa's movies\",\n",
       " 'Oscar (Best Actor)',\n",
       " 'sad',\n",
       " 'on dvr',\n",
       " 'Philip Seymour Hoffman',\n",
       " 'intense',\n",
       " 'plot twist',\n",
       " 'ending',\n",
       " 'Oscar (Best Directing)',\n",
       " 'feel-good',\n",
       " 'Hayao Miyazaki',\n",
       " 'French',\n",
       " 'cars',\n",
       " 'torture',\n",
       " 'cute',\n",
       " 'Russell Crowe',\n",
       " 'Marvel',\n",
       " 'bleak',\n",
       " 'Nudity (Rear)',\n",
       " 'melancholy',\n",
       " 'unrealistic',\n",
       " 'treasure',\n",
       " 'fantasy world',\n",
       " 'fun',\n",
       " 'cynical',\n",
       " 'cerebral',\n",
       " 'homosexuality',\n",
       " 'movie business',\n",
       " 'mockumentary',\n",
       " 'Mel Gibson',\n",
       " 'pirates',\n",
       " 'intelligent',\n",
       " 'Philip K. Dick',\n",
       " 'boxing',\n",
       " 'Michael Caine',\n",
       " 'realistic',\n",
       " 'Sean Connery',\n",
       " 'Bob*ola',\n",
       " 'dreams',\n",
       " 'pixar',\n",
       " 'steampunk',\n",
       " 'mafia',\n",
       " 'Atmospheric',\n",
       " 'Jude Law',\n",
       " 'cheesy',\n",
       " 'existentialism',\n",
       " 'James Bond',\n",
       " 'insanity',\n",
       " 'storytelling',\n",
       " 'business',\n",
       " 'Post apocalyptic',\n",
       " 'car chase',\n",
       " 'Gary Oldman',\n",
       " 'Will Ferrell',\n",
       " 'bad science',\n",
       " 'kidnapping',\n",
       " 'gangsters',\n",
       " 'seen at the cinema',\n",
       " 'british',\n",
       " 'made for TV',\n",
       " 'adapted from:comic',\n",
       " 'poignant',\n",
       " 'crude humor',\n",
       " 'VHS',\n",
       " 'Star Trek',\n",
       " 'arnold',\n",
       " 'beautifully filmed',\n",
       " 'Adam Sandler',\n",
       " 'irreverent',\n",
       " 'Ewan McGregor',\n",
       " 'cannibalism',\n",
       " 'based on a comic',\n",
       " 'nazis',\n",
       " 'cult classic',\n",
       " 'Africa',\n",
       " 'space travel',\n",
       " 'spielberg',\n",
       " 'mentor',\n",
       " 'romantic',\n",
       " 'computers',\n",
       " 'enigmatic',\n",
       " 'humor',\n",
       " 'british comedy',\n",
       " 'NO_FA_GANES',\n",
       " 'suspenseful',\n",
       " 'claustrophobic',\n",
       " 'not funny',\n",
       " 'Oscar (Best Actress)',\n",
       " 'imagination',\n",
       " 'Zooey Deschanel',\n",
       " 'visceral',\n",
       " 'neo-noir',\n",
       " 'sword fight',\n",
       " 'plot holes',\n",
       " 'vampire',\n",
       " 'plot',\n",
       " 'excellent script',\n",
       " 'Angelina Jolie',\n",
       " 'Denzel Washington',\n",
       " 'History',\n",
       " 'Oscar (Best Supporting Actor)',\n",
       " 'Liam Neeson',\n",
       " 'dysfunctional family',\n",
       " 'London',\n",
       " 'DVD',\n",
       " 'dance',\n",
       " 'scary',\n",
       " 'Anthony Hopkins',\n",
       " 'Joseph Gordon-Levitt',\n",
       " 'super-hero',\n",
       " 'powerful ending',\n",
       " 'Keira Knightley',\n",
       " 'male nudity',\n",
       " 'mathematics',\n",
       " 'DIVX',\n",
       " 'science',\n",
       " 'genetics',\n",
       " 'Simon Pegg',\n",
       " 'dogs',\n",
       " 'epic',\n",
       " 'artistic',\n",
       " 'anti-hero',\n",
       " 'bad plot',\n",
       " 'Shakespeare',\n",
       " 'stranded',\n",
       " 'prostitution',\n",
       " 'own',\n",
       " 'sentimental',\n",
       " 'Classic',\n",
       " 'Uma Thurman',\n",
       " 'Batman',\n",
       " 'Drama',\n",
       " 'Heath Ledger',\n",
       " 'Sylvester Stallone',\n",
       " 'seen',\n",
       " 'nature',\n",
       " 'Nicole Kidman',\n",
       " 'disability',\n",
       " 'addiction',\n",
       " '06/11',\n",
       " 'AFI 100',\n",
       " 'monster',\n",
       " 'big budget',\n",
       " 'nostalgic',\n",
       " 'slow paced',\n",
       " 'based on a TV show',\n",
       " 'deadpan',\n",
       " 'trains',\n",
       " 'meditative',\n",
       " 'Holocaust',\n",
       " 'slasher',\n",
       " 'police corruption',\n",
       " 'Jason Statham',\n",
       " 'dark hero',\n",
       " 'btaege',\n",
       " 'vigilante',\n",
       " 'Jack Black',\n",
       " 'characters',\n",
       " 'Kevin Smith',\n",
       " 'masterpiece',\n",
       " 'romantic comedy',\n",
       " 'dancing',\n",
       " 'Sandra Bullock',\n",
       " 'Wes Anderson',\n",
       " 'notable soundtrack',\n",
       " '1960s',\n",
       " 'understated',\n",
       " 'Mafia',\n",
       " 'Owen Wilson',\n",
       " 'Monty Python',\n",
       " 'conspiracy theory',\n",
       " 'Ben Stiller',\n",
       " 'claymation',\n",
       " 'Seth Rogen',\n",
       " 'brutal',\n",
       " 'Los Angeles',\n",
       " 'beautiful scenery',\n",
       " 'detective',\n",
       " 'christianity',\n",
       " 'John Cusack',\n",
       " 'courtroom',\n",
       " 'jesus',\n",
       " 'college',\n",
       " 'writers',\n",
       " 'spoof',\n",
       " 'kung fu',\n",
       " 'David Lynch',\n",
       " 'Steve Carell',\n",
       " '1970s',\n",
       " 'alien invasion',\n",
       " 'John Malkovich',\n",
       " 'film noir',\n",
       " 'amnesia',\n",
       " 'Funny',\n",
       " 'Akira Kurosawa',\n",
       " 'Dustin Hoffman',\n",
       " '19th century',\n",
       " 'Ridley Scott',\n",
       " 'Biography',\n",
       " 'Hugh Jackman',\n",
       " 'Vietnam War',\n",
       " 'pregnancy',\n",
       " 'propaganda',\n",
       " 'samurai',\n",
       " '03/11',\n",
       " 'sex',\n",
       " 'owned',\n",
       " 'cgi',\n",
       " 'medieval',\n",
       " 'India',\n",
       " '01/11',\n",
       " 'bad ending',\n",
       " '05/11',\n",
       " 'Jodie Foster',\n",
       " 'Julia Roberts',\n",
       " 'Based on a TV show',\n",
       " 'siblings',\n",
       " 'biopic',\n",
       " 'Christopher Nolan',\n",
       " '11/10',\n",
       " 'scenic',\n",
       " 'teen movie',\n",
       " 'prison escape',\n",
       " 'Ben Affleck',\n",
       " '02/11',\n",
       " 'disappointing',\n",
       " 'twists & turns',\n",
       " 'based on book',\n",
       " 'ridiculous',\n",
       " 'Jackie Chan',\n",
       " 'Mark Wahlberg',\n",
       " 'lyrical',\n",
       " 'animals',\n",
       " 'Meryl Streep',\n",
       " 'androids',\n",
       " 'dialogue driven',\n",
       " 'father daughter relationship',\n",
       " 'original plot',\n",
       " 'sweet',\n",
       " 'Terry Gilliam',\n",
       " 'remade',\n",
       " \"Eric's Dvds\",\n",
       " 'happy ending',\n",
       " 'Christopher Walken',\n",
       " 'Peter Jackson',\n",
       " 'Star Wars',\n",
       " 'good dialogue',\n",
       " 'Watched',\n",
       " '11/11',\n",
       " 'Roman Polanski',\n",
       " 'eerie',\n",
       " 'women',\n",
       " 'surprise ending',\n",
       " 'script',\n",
       " 'adapted from:play',\n",
       " 'Anamorphic Blow-Up',\n",
       " 'Hugh Grant',\n",
       " 'confusing',\n",
       " 'deliberate',\n",
       " 'ominous',\n",
       " 'anti-war',\n",
       " 'Annemari',\n",
       " 'PG-13',\n",
       " 'unique',\n",
       " 'Cameron Diaz',\n",
       " 'Tommy Lee Jones',\n",
       " 'stylish',\n",
       " '007',\n",
       " 'dragons',\n",
       " 'Kirsten Dunst',\n",
       " 'screwball comedy',\n",
       " 'intellectual',\n",
       " 'depression',\n",
       " 'girlie movie',\n",
       " 'Ellen Page',\n",
       " 'Stereoscopic 3-D',\n",
       " 'Woody Harrelson',\n",
       " 'Special Effects',\n",
       " 'dinosaurs',\n",
       " 'Australia',\n",
       " 'food',\n",
       " 'paranoid',\n",
       " 'political',\n",
       " 'George Lucas',\n",
       " 'island',\n",
       " 'dramatic',\n",
       " 'aviation',\n",
       " 'stand-up comedy',\n",
       " 'Middle East',\n",
       " 'watch the credits',\n",
       " 'Emma Stone',\n",
       " 'Below R',\n",
       " 'talky',\n",
       " 'haunted house',\n",
       " 'Jean Reno',\n",
       " 'animated',\n",
       " 'courtroom drama',\n",
       " 'etaege',\n",
       " 'want to see again',\n",
       " 'obsession',\n",
       " 'AFI 100 (Laughs)',\n",
       " 'Anne Hathaway',\n",
       " 'USA',\n",
       " 'motorcycle',\n",
       " 'library vhs',\n",
       " 'environmental',\n",
       " 'lawyers',\n",
       " 'Fantasy',\n",
       " 'guns',\n",
       " 'erotic',\n",
       " 'poverty',\n",
       " 'childhood',\n",
       " 'brutality',\n",
       " 'Futuristmovies.com',\n",
       " 'Jeff Bridges',\n",
       " 'spy',\n",
       " 'John Travolta',\n",
       " 'photography',\n",
       " 'time loop',\n",
       " '12/10',\n",
       " 'Charlie Kaufman',\n",
       " 'television',\n",
       " 'technology',\n",
       " 'Ryan Gosling',\n",
       " 'computer animation',\n",
       " 'absurd',\n",
       " '1930s',\n",
       " 'Latin America',\n",
       " 'baseball',\n",
       " 'submarine',\n",
       " 'genius',\n",
       " 'child abuse',\n",
       " 'divorce',\n",
       " 'Jake Gyllenhaal',\n",
       " 'hackers',\n",
       " 'paranoia',\n",
       " 'China',\n",
       " 'Milla Jovovich',\n",
       " 'assassination',\n",
       " 'sexual',\n",
       " 'audience intelligence underestimated',\n",
       " 'fighting',\n",
       " 'werewolves',\n",
       " 'Sean Penn',\n",
       " 'Oscar (Best Supporting Actress)',\n",
       " 'menacing',\n",
       " 'feel good movie',\n",
       " 'christian',\n",
       " 'New York',\n",
       " 'Jane Austen',\n",
       " 'great cinematography',\n",
       " 'video game adaptation',\n",
       " 'Sigourney Weaver',\n",
       " 'David Fincher',\n",
       " 'video games',\n",
       " 'wedding',\n",
       " 'Alan Rickman',\n",
       " 'redemption',\n",
       " 'stop motion',\n",
       " 'investigation',\n",
       " 'Jennifer Aniston',\n",
       " 'scifi',\n",
       " 'football',\n",
       " '12/11',\n",
       " 'goofy',\n",
       " 'immortality',\n",
       " 'M. Night Shyamalan',\n",
       " 'Adventure',\n",
       " 'alcoholism',\n",
       " 'South America',\n",
       " 'archaeology',\n",
       " 'Kate Winslet',\n",
       " 'revolution',\n",
       " 'rebellion',\n",
       " 'marriage',\n",
       " 'Quirky',\n",
       " 'Lars von Trier',\n",
       " '1950s',\n",
       " 'splatter',\n",
       " 'Gene Hackman',\n",
       " '10/10',\n",
       " 'royalty',\n",
       " 'AFI 100 (Thrills)',\n",
       " 'Colin Farrell',\n",
       " 'suburbia',\n",
       " 'BGAB LRC',\n",
       " 'Vin Diesel',\n",
       " 'Robert Rodriguez',\n",
       " 'Crime',\n",
       " 'alter ego',\n",
       " 'not available from Netflix',\n",
       " 'strong female lead',\n",
       " 'off-beat comedy',\n",
       " 'Musical',\n",
       " 'end of the world',\n",
       " 'dragon',\n",
       " 'good acting',\n",
       " 'Charlize Theron',\n",
       " 'Eddie Murphy',\n",
       " 'fight scenes',\n",
       " 'not a movie',\n",
       " 'Hitchcock',\n",
       " 'cross dressing',\n",
       " 'marijuana',\n",
       " 'special effects',\n",
       " 'Jennifer Lawrence',\n",
       " 'Sam Rockwell',\n",
       " \"so bad it's good\",\n",
       " 'Guy Ritchie',\n",
       " 'Zach Galifianakis',\n",
       " 'interesting',\n",
       " 'Horror',\n",
       " 'Kevin Costner',\n",
       " 'mars',\n",
       " 'mythology',\n",
       " 'pretentious',\n",
       " 'immigrants',\n",
       " 'grim',\n",
       " 'culture clash',\n",
       " 'quotable',\n",
       " 'Clive Owen',\n",
       " '01/12',\n",
       " 'heartbreaking',\n",
       " 'Germany',\n",
       " 'james bond',\n",
       " 'forceful',\n",
       " 'Dark',\n",
       " 'justice',\n",
       " 'Paul Rudd',\n",
       " 'WWII',\n",
       " 'Catholicism',\n",
       " 'Animation',\n",
       " 'mother-son relationship',\n",
       " 'dystopic future',\n",
       " 'nudity',\n",
       " 'spaghetti western',\n",
       " 'virus',\n",
       " 'action packed',\n",
       " 'realism',\n",
       " 'Michael Cera',\n",
       " 'dark fantasy',\n",
       " 'independent film',\n",
       " 'incest',\n",
       " 'zombie',\n",
       " 'self discovery',\n",
       " 'bollywood',\n",
       " 'Helena Bonham Carter',\n",
       " 'wry',\n",
       " '3D',\n",
       " 'hitman',\n",
       " 'Mila Kunis',\n",
       " 'cold war',\n",
       " 'slapstick',\n",
       " 'Las Vegas',\n",
       " 'Sexualized violence',\n",
       " 'childish',\n",
       " 'AFI 100 (Cheers)',\n",
       " 'multiple roles',\n",
       " 'Biblical',\n",
       " 'better than expected',\n",
       " 'KAF',\n",
       " 'Ireland',\n",
       " 'secrets',\n",
       " 'witch',\n",
       " 'gruesome',\n",
       " 'adolescence',\n",
       " 'War',\n",
       " 'road movie',\n",
       " 'Best of Rotten Tomatoes: All Time',\n",
       " 'isolation',\n",
       " 'Cate Blanchett',\n",
       " 'Gwyneth Paltrow',\n",
       " 'Disney animated feature',\n",
       " 'melancholic',\n",
       " 'ocean',\n",
       " 'gambling',\n",
       " 'alien',\n",
       " 'short-term memory loss',\n",
       " 'Sci-Fi',\n",
       " 'biographical',\n",
       " 'great performances',\n",
       " 'Hollywood',\n",
       " 'strippers',\n",
       " 'Colin Firth',\n",
       " 'blindness',\n",
       " 'spies',\n",
       " 'AFI 100 (Movie Quotes)',\n",
       " 'desert',\n",
       " 'con artists',\n",
       " 'Orson Welles',\n",
       " 'demons',\n",
       " 'Golden Palm',\n",
       " 'Matthew McConaughey',\n",
       " 'Oscar (Best Effects - Visual Effects)',\n",
       " 'Daniel Craig',\n",
       " 'Amy Adams',\n",
       " 'oscar (best cinematography)',\n",
       " 'sexy',\n",
       " 'complex characters',\n",
       " '100 Essential Female Performances',\n",
       " 'Drew Barrymore',\n",
       " 'over the top',\n",
       " 'brothers',\n",
       " 'unlikeable characters',\n",
       " 'good versus evil',\n",
       " 'Willem Dafoe',\n",
       " 'Rachel McAdams',\n",
       " 'ghosts/afterlife',\n",
       " 'compassionate',\n",
       " 'intimate',\n",
       " 'Ralph Fiennes',\n",
       " 'Guillermo del Toro',\n",
       " 'Ryan Reynolds',\n",
       " 'bad script',\n",
       " 'Steve Buscemi',\n",
       " 'bullying',\n",
       " 'cheerleading',\n",
       " 'nerds',\n",
       " 'Surreal',\n",
       " 'Funny as hell',\n",
       " '04/11',\n",
       " 'undercover cop',\n",
       " 'Peter Sellers',\n",
       " 'Underrated',\n",
       " 'schizophrenia',\n",
       " 'werewolf',\n",
       " 'need to own',\n",
       " 'thought provoking',\n",
       " 'confrontational',\n",
       " 'Vietnam',\n",
       " 'My movies',\n",
       " 'Reese Witherspoon',\n",
       " 'downbeat',\n",
       " 'treasure hunt',\n",
       " 'Ingmar Bergman',\n",
       " 'James Cameron',\n",
       " 'Jonah Hill',\n",
       " 'Tarantino',\n",
       " 'Werner Herzog',\n",
       " 'fashion',\n",
       " 'boarding school',\n",
       " 'Amazing Cinematography',\n",
       " 'Oscar (Best Foreign Language Film)',\n",
       " 'Jet Li',\n",
       " 'Ennio Morricone',\n",
       " 'mad scientist',\n",
       " 'James Stewart',\n",
       " 'ballet',\n",
       " 'harry potter',\n",
       " 'Robert Redford',\n",
       " 'jus+san',\n",
       " 'Julianne Moore',\n",
       " 'twist',\n",
       " 'pointless',\n",
       " 'Michael Fassbender',\n",
       " 'infidelity',\n",
       " 'blood',\n",
       " 'strange',\n",
       " 'Oscar Winner',\n",
       " 'Orlando Bloom',\n",
       " 'US President',\n",
       " 'World War I',\n",
       " 'Dreamworks',\n",
       " 'Vincent Price',\n",
       " 'add to prospects list',\n",
       " 'disaster',\n",
       " 'Sergio Leone',\n",
       " '08/10',\n",
       " 'teenagers',\n",
       " 'literary adaptation',\n",
       " 'physics',\n",
       " 'Michael Douglas',\n",
       " 'superheroes',\n",
       " 'Winona Ryder',\n",
       " 'alternate universe',\n",
       " 'hacking',\n",
       " 'slavery',\n",
       " 'bank robbery',\n",
       " 'Adrien Brody',\n",
       " 'CIA',\n",
       " 'fast paced',\n",
       " 'oscar (best directing)',\n",
       " 'nuclear war',\n",
       " 'Naomi Watts',\n",
       " 'Italy',\n",
       " 'Ethan Hawke',\n",
       " '03/10',\n",
       " 'stereotypes',\n",
       " '1920s',\n",
       " 'christmas',\n",
       " 'moody',\n",
       " 'school',\n",
       " 'IMAX Digital only',\n",
       " 'great dialogue',\n",
       " 'Audrey Hepburn',\n",
       " 'Teen movie',\n",
       " '18th century',\n",
       " 'Joaquin Phoenix',\n",
       " '2D animation',\n",
       " 'Viggo Mortensen',\n",
       " 'redbox',\n",
       " 'biting',\n",
       " 'classical music',\n",
       " 'Cary Grant',\n",
       " 'Dynamic CGI Action',\n",
       " 'James Franco',\n",
       " 'homophobia',\n",
       " 'literate',\n",
       " 'Kristen Stewart',\n",
       " 'high fantasy',\n",
       " 'Humphrey Bogart',\n",
       " 'unintentional comedy',\n",
       " 'cyborgs',\n",
       " 'surveillance',\n",
       " 'Scary Movies To See on Halloween',\n",
       " 'CGI',\n",
       " 'Daniel Day-Lewis',\n",
       " 'Jim Jarmusch',\n",
       " 'Rachel Weisz',\n",
       " 'Scotland',\n",
       " 'Maggie Gyllenhaal',\n",
       " 'gangs',\n",
       " 'San Francisco',\n",
       " 'Paul Newman',\n",
       " 'art',\n",
       " 'Gfei own it',\n",
       " 'passionate',\n",
       " \"Sven's to see list\",\n",
       " 'Brazil',\n",
       " 'weak plot',\n",
       " 'Mel Brooks',\n",
       " '06/10',\n",
       " 'vhs',\n",
       " 'cloning',\n",
       " 'IMAX DMR',\n",
       " '80s',\n",
       " 'Patrick Stewart',\n",
       " 'Michael Crichton',\n",
       " 'workplace',\n",
       " 'super hero',\n",
       " 'Divx1',\n",
       " 'disease',\n",
       " 'hotel',\n",
       " 'earnest',\n",
       " 'psychedelic',\n",
       " 'transgender',\n",
       " 'Bond',\n",
       " 'Forest Whitaker',\n",
       " 'colonialism',\n",
       " 'beautiful cinematography',\n",
       " 'David Cronenberg',\n",
       " 'Spherical Blow-Up',\n",
       " 'marvel',\n",
       " 'IMAX DMR 3-D',\n",
       " 'PG13',\n",
       " 'Gay Lead Character',\n",
       " 'pornography',\n",
       " 'youtube',\n",
       " 'New Zealand',\n",
       " 'coen brothers',\n",
       " 'Michael J. Fox',\n",
       " 'Vince Vaughn',\n",
       " 'Nostalgia Critic',\n",
       " 'Sci-fi',\n",
       " 'family bonds',\n",
       " 'voyeurism',\n",
       " 'gangster',\n",
       " '05/10',\n",
       " 'Meg Ryan',\n",
       " 'Oliver Stone',\n",
       " 'Gerard Butler',\n",
       " 'wizards',\n",
       " 'Kevin Bacon',\n",
       " 'Ben Kingsley',\n",
       " 'French Film',\n",
       " 'amazing photography',\n",
       " 'Bruce Campbell',\n",
       " 'Emma Thompson',\n",
       " 'soccer',\n",
       " 'Antonio Banderas',\n",
       " 'Bradley Cooper',\n",
       " 'cia',\n",
       " 'somber',\n",
       " 'product placement',\n",
       " 'afterlife',\n",
       " 'austere',\n",
       " 'Kurt Russell',\n",
       " 'wired 50 greatest soundtracks',\n",
       " 'Marlon Brando',\n",
       " 'REDBOX',\n",
       " 'Pierce Brosnan',\n",
       " 'orphans',\n",
       " 'Jason Segel',\n",
       " 'vengeance',\n",
       " 'PG',\n",
       " 'husband-wife relationship',\n",
       " 'Mark Ruffalo',\n",
       " 'drinking',\n",
       " 'underrated',\n",
       " 'Film Noir',\n",
       " 'nostalgia',\n",
       " 'Michael Moore',\n",
       " 'Tolkien',\n",
       " 'Revenge',\n",
       " 'Aardman',\n",
       " 'heroine in tight suit',\n",
       " 'modern fantasy',\n",
       " 'Christopher Lloyd',\n",
       " 'unpredictable',\n",
       " '01/10',\n",
       " 'jazz',\n",
       " 'Good Romantic Comedies',\n",
       " 'Jennifer Connelly',\n",
       " 'Mystery',\n",
       " 'flashbacks',\n",
       " 'Netflix Streaming',\n",
       " 'no plot',\n",
       " 'smart',\n",
       " 'John Goodman',\n",
       " 'Black comedy',\n",
       " 'paranormal',\n",
       " 'Mexico',\n",
       " 'Paul Giamatti',\n",
       " 'Music',\n",
       " 'complicated plot',\n",
       " ...]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_tag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 为标签做索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "tag_name_to_index_dict = {}\n",
    "tag_index_to_name_dict = {}\n",
    "\n",
    "\n",
    "for index,tag_name  in enumerate(top_tag):\n",
    "    tag_name_to_index_dict[tag_name] = index\n",
    "    tag_index_to_name_dict[index] = tag_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>tag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65</td>\n",
       "      <td>208</td>\n",
       "      <td>428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65</td>\n",
       "      <td>353</td>\n",
       "      <td>428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65</td>\n",
       "      <td>521</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65</td>\n",
       "      <td>592</td>\n",
       "      <td>428</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>465557</th>\n",
       "      <td>138446</td>\n",
       "      <td>7164</td>\n",
       "      <td>1852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465558</th>\n",
       "      <td>138446</td>\n",
       "      <td>7164</td>\n",
       "      <td>18</td>\n",
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       "    <tr>\n",
       "      <th>465560</th>\n",
       "      <td>138446</td>\n",
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       "    <tr>\n",
       "      <th>465561</th>\n",
       "      <td>138446</td>\n",
       "      <td>55999</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465562</th>\n",
       "      <td>138446</td>\n",
       "      <td>55999</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>367091 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        userId  movieId   tag\n",
       "1           65      208   428\n",
       "2           65      353   428\n",
       "3           65      521   232\n",
       "4           65      592   428\n",
       "5           65      668   720\n",
       "...        ...      ...   ...\n",
       "465557  138446     7164  1852\n",
       "465558  138446     7164    18\n",
       "465560  138446    55999  1272\n",
       "465561  138446    55999    11\n",
       "465562  138446    55999   277\n",
       "\n",
       "[367091 rows x 3 columns]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tag['tag'] = df_tag['tag'].apply(lambda tag:tag_name_to_index_dict[tag])\n",
    "df_tag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>tag_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65</td>\n",
       "      <td>208</td>\n",
       "      <td>428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65</td>\n",
       "      <td>353</td>\n",
       "      <td>428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65</td>\n",
       "      <td>521</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65</td>\n",
       "      <td>592</td>\n",
       "      <td>428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>65</td>\n",
       "      <td>668</td>\n",
       "      <td>720</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  movie_id  tag_index\n",
       "1       65       208        428\n",
       "2       65       353        428\n",
       "3       65       521        232\n",
       "4       65       592        428\n",
       "5       65       668        720"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tag.columns = ['user_id','movie_id','tag_index']\n",
    "df_tag.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "用户数 6731\n",
      "电影数 16934\n",
      "标签数 2952\n"
     ]
    }
   ],
   "source": [
    "user_quantity = len(df_tag['user_id'].unique())\n",
    "movie_quantity = len(df_tag['movie_id'].unique())\n",
    "tag_quantity = len(top_tag)\n",
    "print('用户数',user_quantity)\n",
    "print('电影数',movie_quantity)\n",
    "print('标签数',tag_quantity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 简历用户标签矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100 3200 3300 3400 3500 3600 3700 3800 3900 4000 4100 4200 4300 4400 4500 4600 4700 4800 4900 5000 5100 5200 5300 5400 5500 5600 5700 5800 5900 6000 6100 6200 6300 6400 6500 6600 6700 "
     ]
    }
   ],
   "source": [
    "user_id_to_index_dict = {}\n",
    "user_index_to_id_dict = {}\n",
    "\n",
    "# 初始化一个0矩阵\n",
    "user_tag_array = np.zeros(shape=(user_quantity,tag_quantity),dtype='i1')\n",
    "\n",
    "for index,(user_id,groupby_userid) in enumerate(df_tag.groupby('user_id')):\n",
    "    user_id_to_index_dict[user_id] = index\n",
    "    user_index_to_id_dict[index] = user_id\n",
    "    \n",
    "    tag_value_counts = groupby_userid['tag_index'].value_counts()\n",
    "    line_data = np.zeros(shape=tag_quantity,dtype='i1')\n",
    "    for tag_index in tag_value_counts.index:\n",
    "        line_data[tag_index] = tag_value_counts[tag_index]\n",
    "    user_tag_array[index] = line_data\n",
    "    if index % 100 == 0:print(index,end=' ')\n",
    "\n",
    "\n",
    "# use_tag_array = np.zeros(shape = (user_quantity,tag_quantity),dtype='i1')\n",
    "\n",
    "# for index,(user_id,groupby_userid) in enumerate(df_tag.groupby('user_id')):\n",
    "\n",
    "#     a = groupby_userid['tag_index'].value_counts()\n",
    "    \n",
    "#     line_data = np.zeros(shape = (tag_quantity),dtype='i1')\n",
    "#     for tag_index in a.index:\n",
    "#         line_data[tag_index] = a[tag_index]\n",
    "#     use_tag_array[index] = line_data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100 3200 3300 3400 3500 3600 3700 3800 3900 4000 4100 4200 4300 4400 4500 4600 4700 4800 4900 5000 5100 5200 5300 5400 5500 5600 5700 5800 5900 6000 6100 6200 6300 6400 6500 6600 6700 6800 6900 7000 7100 7200 7300 7400 7500 7600 7700 7800 7900 8000 8100 8200 8300 8400 8500 8600 8700 8800 8900 9000 9100 9200 9300 9400 9500 9600 9700 9800 9900 10000 10100 10200 10300 10400 10500 10600 10700 10800 10900 11000 11100 11200 11300 11400 11500 11600 11700 11800 11900 12000 12100 12200 12300 12400 12500 12600 12700 12800 12900 13000 13100 13200 13300 13400 13500 13600 13700 13800 13900 14000 14100 14200 14300 14400 14500 14600 14700 14800 14900 15000 15100 15200 15300 15400 15500 15600 15700 15800 15900 16000 16100 16200 16300 16400 16500 16600 16700 16800 16900 "
     ]
    }
   ],
   "source": [
    "# movie_id_to_index_dict = {}\n",
    "# movie_index_to_id_dict = {}\n",
    "\n",
    "# # 初始化一个0矩阵\n",
    "# movie_tag_array = np.zeros(shape=(movie_quantity,tag_quantity),dtype='i1')\n",
    "\n",
    "# for index,(movie_id,groupby_movieid) in enumerate(df_tag.groupby('movie_id')):\n",
    "#     movie_index_to_id_dict[index] = movie_id\n",
    "#     movie_id_to_index_dict[movie_id] = index\n",
    "    \n",
    "#     tag_value_counts = groupby_movieid['tag_index'].value_counts()\n",
    "#     line_data = np.zeros(shape=tag_quantity,dtype='i1')\n",
    "#     for tag_index in tag_value_counts.index:\n",
    "#         line_data[tag_index] = tag_value_counts[tag_index]\n",
    "#     movie_tag_array[index] = line_data\n",
    "#     if index % 100 == 0:print(index,end=' ')\n",
    "        \n",
    "        \n",
    "movie_id_to_index_dict = {}\n",
    "movie_index_to_id_dict = {}\n",
    "\n",
    "movie_tag_array = np.zeros(shape = (movie_quantity,tag_quantity),dtype='i1')\n",
    "\n",
    "for index,(movie_id,groupby_movieid) in enumerate(df_tag.groupby('movie_id')):\n",
    "    movie_id_to_index_dict[movie_id] = index\n",
    "    movie_index_to_id_dict[index] = movie_id\n",
    "    \n",
    "    tag_value_counts = groupby_movieid['tag_index'].value_counts()\n",
    "    line_data = np.zeros(shape=tag_quantity,dtype='i1')\n",
    "    for tag_index in tag_value_counts.index:\n",
    "        line_data[tag_index] = tag_value_counts[tag_index]\n",
    "    movie_tag_array[index] = line_data\n",
    "    if index % 100 == 0:print(index,end=' ')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>tag_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>175</td>\n",
       "      <td>428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>293</td>\n",
       "      <td>428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>432</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>487</td>\n",
       "      <td>428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>536</td>\n",
       "      <td>720</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  movie_id  tag_index\n",
       "1        0       175        428\n",
       "2        0       293        428\n",
       "3        0       432        232\n",
       "4        0       487        428\n",
       "5        0       536        720"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tag['user_id'] = df_tag['user_id'].apply(lambda user_id : user_id_to_index_dict[user_id])\n",
    "df_tag['movie_id'] = df_tag['movie_id'].apply(lambda movie_id: movie_id_to_index_dict[movie_id])\n",
    "df_tag.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_index</th>\n",
       "      <th>movie_index</th>\n",
       "      <th>tag_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>175</td>\n",
       "      <td>428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>293</td>\n",
       "      <td>428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>432</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>487</td>\n",
       "      <td>428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>536</td>\n",
       "      <td>720</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_index  movie_index  tag_index\n",
       "1           0          175        428\n",
       "2           0          293        428\n",
       "3           0          432        232\n",
       "4           0          487        428\n",
       "5           0          536        720"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tag.columns = ['user_index','movie_index','tag_index']\n",
    "df_tag.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  用户电影矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100 3200 3300 3400 3500 3600 3700 3800 3900 4000 4100 4200 4300 4400 4500 4600 4700 4800 4900 5000 5100 5200 5300 5400 5500 5600 5700 5800 5900 6000 6100 6200 6300 6400 6500 6600 6700 "
     ]
    }
   ],
   "source": [
    "user_movie_array = np.zeros(shape = (user_quantity,movie_quantity),dtype='i1')\n",
    "\n",
    "\n",
    "for user_index,groupby_userindex in df_tag.groupby('user_index'):\n",
    "    \n",
    "    a = groupby_userindex['movie_index'].unique().tolist()\n",
    "    line_data = np.zeros(shape = (movie_quantity))\n",
    "    for index in a:\n",
    "        line_data[index] = 1\n",
    "    user_movie_array[user_index] = line_data\n",
    "    if user_index % 100 == 0:print(user_index,end=' ')\n",
    "        \n",
    "        \n",
    "        \n",
    "\n",
    "#     print(a)\n",
    "#     print(groupby_userindex)\n",
    "#     break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int8)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_movie_array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###   对热门标签做惩罚"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-125-8937846afd21>:3: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  user_tag_array2 = np.around(user_tag_array /(np.log(1 +(user_tag_array>0).astype(int).sum(axis=0))),3)\n",
      "<ipython-input-125-8937846afd21>:3: RuntimeWarning: invalid value encountered in true_divide\n",
      "  user_tag_array2 = np.around(user_tag_array /(np.log(1 +(user_tag_array>0).astype(int).sum(axis=0))),3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.   , 0.   , 0.   , ..., 0.   , 0.   , 0.   ],\n",
       "       [0.   , 0.   , 0.   , ..., 0.   , 0.   , 0.   ],\n",
       "       [0.   , 0.   , 0.   , ..., 0.   , 0.   , 0.   ],\n",
       "       ...,\n",
       "       [0.   , 0.   , 0.767, ..., 0.   , 0.   , 0.   ],\n",
       "       [0.   , 0.156, 0.   , ..., 0.   , 0.   , 0.   ],\n",
       "       [0.   , 0.   , 0.   , ..., 0.   , 0.   , 0.   ]])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_tag_array\n",
    "\n",
    "user_tag_array2 = np.around(user_tag_array /(np.log(1 +(user_tag_array>0).astype(int).sum(axis=0))),3)\n",
    "user_tag_array2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对热门商品做惩罚"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.   , 0.   , 0.   , ..., 0.   , 0.   , 0.   ],\n",
       "       [0.   , 0.819, 0.   , ..., 0.   , 0.   , 0.   ],\n",
       "       [0.   , 0.   , 0.   , ..., 0.   , 0.   , 0.   ],\n",
       "       ...,\n",
       "       [0.   , 0.   , 0.   , ..., 0.   , 0.   , 0.   ],\n",
       "       [0.   , 0.   , 0.   , ..., 0.   , 0.   , 0.   ],\n",
       "       [0.   , 0.   , 0.   , ..., 0.   , 0.   , 0.   ]])"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_tag_array2 = np.around(movie_tag_array / np.log(1 + np.array([user_movie_array.sum(axis=0)]).T),3)\n",
    "movie_tag_array2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 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     ]
    },
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     ]
    }
   ],
   "source": [
    "user_movie_fav_array = np.zeros(shape=(user_quantity,movie_quantity))\n",
    "\n",
    "for user_index in range(user_quantity):\n",
    "    # 拿到用户打过的标签索引向量\n",
    "    user_rated_tag_indexs = np.where(user_tag_array2[user_index] >0)[0].tolist()\n",
    "    # 用户对标签的喜好程度\n",
    "    user_rated_tag_values = user_tag_array2[user_index][user_rated_tag_indexs]\n",
    "    # 被打过这些标签的电影索引\n",
    "    taged_movie_indexs = np.where((movie_tag_array2[:,user_rated_tag_indexs] > 0).astype(int).sum(axis=1) > 0)[0].tolist()\n",
    "#     print(taged_movie_indexs)\n",
    "    sub_movie_tag_array = movie_tag_array2[taged_movie_indexs][:,user_rated_tag_indexs]\n",
    "#     print(sub_movie_tag_array)\n",
    "    movies_fav = np.around(np.dot(sub_movie_tag_array,np.array([user_rated_tag_values]).T),3).T[0].tolist()\n",
    "    line_data = np.zeros(shape=movie_quantity)\n",
    "    for i,movie_index in enumerate(taged_movie_indexs):\n",
    "        line_data[movie_index] = movies_fav[i]\n",
    "    user_movie_fav_array[user_index] = line_data\n",
    "    print(user_index,end=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_movie_fav_array = np.zeros(shape=(user_quantity,movie_quantity))\n",
    "\n",
    "\n",
    "for user_index in range(user_quantity):\n",
    "    # 拿到用户打过的标签索引向量\n",
    "    user_rated_tag_indexs = np.where(user_tag_array2[user_index] >0)[0].tolist()\n",
    "    # 用户对标签的喜好程度\n",
    "    user_rated_tag_values = user_tag_array2[user_index][user_rated_tag_indexs]\n",
    "    # 被打过这些标签的电影索引\n",
    "    taged_movie_indexs = np.where((movie_tag_array2[:,user_rated_tag_indexs] > 0).astype(int).sum(axis=1) > 0)[0].tolist()\n",
    "#     print(taged_movie_indexs)\n",
    "    sub_movie_tag_array = movie_tag_array2[taged_movie_indexs][:,user_rated_tag_indexs]\n",
    "#     print(sub_movie_tag_array)\n",
    "    movies_fav = np.around(np.dot(sub_movie_tag_array,np.array([user_rated_tag_values]).T),3).T[0].tolist()\n",
    "    line_data = np.zeros(shape=movie_quantity)\n",
    "    for i,movie_index in enumerate(taged_movie_indexs):\n",
    "        line_data[movie_index] = movies_fav[i]\n",
    "    user_movie_fav_array[user_index] = line_data\n",
    "#     print(user_index,end=' ')\n",
    "#     print(user_rated_tag_indexs)\n",
    "#     print(user_rated_tag_values)\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 生成用户推荐表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_recommend = {}\n",
    "\n",
    "for user_index in range(user_quantity):\n",
    "    user_recommend[user_index] = np.where(user_movie_fav_array>2)[0].tolist()\n",
    "  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rating = pd.read_csv('./data/rating.csv',usecols=[0,1,2])\n",
    "df_rating.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rating.columns = ['user_id','movie_id','rating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def deal_with_userid(user_id):\n",
    "    if user_id in user_id_to_index_dict.keys():\n",
    "        return user_id_to_index_dict[user_id]\n",
    "    else:\n",
    "        return None\n",
    "    \n",
    "def deal_with_movieid(movie_id):\n",
    "    if movie_id in movie_id_to_index_dict.keys():\n",
    "        return movie_id_to_index_dict[movie_id]\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "df_rating['user_id'] = df_rating['user_id'].apply(deal_with_userid)\n",
    "df_rating['movie_id'] = df_rating['movie_id'].apply(deal_with_movieid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rating = df_rating.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rating['user_id'] = df_rating['user_id'].astype(int)\n",
    "df_rating['movie_id'] = df_rating['movie_id'].astype(int)\n",
    "df_rating.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rating.columns = ['user_index','movie_index','rating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_fav = {}\n",
    "for user_index,groupby_userindex in df_rating.groupby('user_index'):\n",
    "    movies_rating = groupby_userindex.groupby('movie_index')['rating'].mean()\n",
    "    fav_movie_indexs = movies_rating[\n",
    "        movies_rating >= 3\n",
    "    ].index.tolist()\n",
    "    user_fav[user_index] = fav_movie_indexs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "union_quantity = 0\n",
    "recommend_quantity = 0\n",
    "fav_quantity = 0\n",
    "\n",
    "for user_index in user_recommend.keys():\n",
    "    if user_index in user_fav.keys():\n",
    "        union_quantity += len(\n",
    "            set(user_recommend[user_index]) & set(user_fav[user_index])\n",
    "        )\n",
    "        recommend_quantity += len(user_recommend[user_index])\n",
    "        fav_quantity += len(user_fav[user_index])\n",
    "\n",
    "print('precision',union_quantity / recommend_quantity)\n",
    "print('recall',union_quantity / fav_quantity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 最后几行代码 电脑带不动 显示内存不足"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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